SAFLOOR: Smart Fall Detection System for the Elderly

IRJET, 2021

Falls are a serious health concern for the elderly who live in the neighborhood. For more than two decades, medical institutions have conducted substantial research on falls in order to attenuate their impact (e.g., loss of freedom, fear of falling, etc.) and minimize their consequences (e.g., Cost of hospitalization, etc.). However, the subject of elderly people falling has piqued the interest of scientists as well as health experts. Indeed, falls have been the subject of several scientific investigations as well as the aim of numerous commercial products from academia and industry. These studies have addressed the issue by employing fall detection algorithms that have exhausted a range of sensor methods. Recently, researchers have moved their focus to fall prevention, with the goal of detecting falls before they occur. The chief contribution of this study in this matter is to offer a thorough outline of elderly falls and to recommend an all-purpose solution of fall-related systems based on real time posture estimation. Based on this common ground classification, an extensive research plan ranging from fall detection to fall prevention technologies was also carried out. Data processing techniques are also featured in both the fall detection and fall prevention courses. The goal of this effort is to provide medical technologists in the field of public health with a good understanding of fall-related systems.

Arabian Journal for Science and Engineering, 2018

This paper proposes to develop of a low cost fall detection system to precisely detect an event where an elderly person accidentally falls. The fall detection algorithm compares the acceleration with Lower fall Threshold (LFT) and Upper Fall Threshold (UFT) values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added in our proposed method to enhance the performance and accuracy of it. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. The smartphone application will ensure that the notification is delivered to the elderly person's relatives so medical attention can be provided with minimal delay. The system was tested with volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public data and it also achieved the same percentage in sensitivity and accuracy as in our recorded data.

Sustainability

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient ...

International Journal of Science Technology & Engineering

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. The system is capable of monitoring ELDERLY PEOPLE in real time. The system, including calibration of accelerometers and measurement is explained in detail. This fall detection system is designed to detect the accidental fall of the elderly and alert the carers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the care taker.

Procedia Computer Science, 2016

Falls among older people remain a very important public healthcare issue. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The last module of F2D is the location module which makes our system very useful for nursing homes that host elderly people. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By testing with real data and achieving an accuracy of 96.01% we have a fall detection system ready to be deployed on the market and by adding the location module we can provide it to nursing homes for elderly people. c

International Conference on Information and Communication Technologies: From Theory to Applications, 2006

Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is

2010

The problem of fall detection in elderly patients is particularly critical in persons who live alone or are alone most of the day. The use of information and communication technologies to facilitate their autonomy is a clear example of how technological advances can improve the quality of life of dependent people. This article presents a prototype developed with a low cost device (the gamepad of a known video console) using its Bluetooth communication capabilities and built-in accelerometer. The latter is much more sensitive than other similar devices integrated in mobile phones and much cheaper than industrial accelerometers. Besides its stand-alone use, the system can be connected to a generic remote monitoring system that has been developed as a software product line for use in aged people's residences.

International Journal on Smart Sensing and Intelligent Systems

It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting falls and triggering notification alarms calling for help as soon as falls occur in order to diminish fall consequences. Furthermore, fall likelihood prediction systems have been emerged lately based on the manipulation of the medical and behavioral history of elderly patients in order to predict the possibility of falls occurrence. Accordingly, response from caregivers may be triggered prior to most fall occurrences and accordingly prevent falls from taking place. This paper presents an extensive review for the state-of-theart trends and technologies of fall detection and prevention systems assisting the elderly people and

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities.

Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 2011

Within the project "eHome" a prototype of an assistive home system was developed, aiming to prolong the independent life of elderly people at home. Besides communication, e-access and safety relevant features, a core part of this system is an automatic fall detection, which utilizes floor-mounted accelerometers to gather body-sound signals that typically occur during a human fall. This approach targets to avoid acceptance, usability and reliability issues of available body-mounted fall detectors. The system was developed with focus on practical applicability, reliability and exploitability. The prototype was evaluated successfully in laboratory and during 507 days in reallife at homes of persons from the target group. During the laboratory trials a sensitivity of 87% and a specificity of 97.7% could be achieved for a defined fall scenario and across four tested floors. Further research is suggested to investigate floor dependencies of the fall detection performance.